Initial QC on KD6 Essential Data - Multi-UTR Genes
Mervin M Fansler
13 September 2022
Purpose
Here we perform a second pass look at the data, similar to the first pass, but focusing specifically on multi-UTR genes.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(cowplot)
library(plotly)
library(ggbeeswarm)
library(Matrix)
library(matrixStats)
library(SingleCellExperiment)Parameters
set.seed(20210818)
FILE_SCE_TX="data/sce/kd6_essential.annot.txs.Rds"Data
Loading
sce <- readRDS(FILE_SCE_TX) %>% `[`(rowData(.)$utr_type_raw == 'multi',)Preprocessing
Among our statistics, we will compute the percentage of UMIs corresponding to specific isoform classes, including IPA, proximal, and distal, in multi-UTR genes.
df_cells <- colData(sce) %>% as_tibble %>%
mutate(umis_total=colSums(counts(sce)),
pct_ipa=colSums(counts(sce[rowData(sce)$is_ipa,]))/umis_total,
pct_proximal=colSums(counts(sce[rowData(sce)$is_proximal,]))/umis_total,
pct_distal=colSums(counts(sce[rowData(sce)$is_distal,]))/umis_total)Target Analysis
We will explore whether any perturbations have effects on basic transcriptome characteristics. These will also be compared against each other.
df_targets <- df_cells %>%
group_by(target_gene, target_gene_id, sgID_AB) %>%
summarize(n_cells=dplyr::n(),
mean_umis_total=mean(umis_total),
pct_ipa=weighted.mean(pct_ipa, umis_total),
pct_proximal=weighted.mean(pct_proximal, umis_total),
pct_distal=weighted.mean(pct_distal, umis_total), .groups='drop')IPA
df_targets %>%
mutate(sgID_AB=fct_reorder(sgID_AB, -pct_ipa)) %>%
ggplot(aes(x=sgID_AB, y=pct_ipa, fill=target_gene == "non-targeting")) +
geom_bar(stat='identity') +
scale_y_continuous(expand=c(0,0,0.05,0)) +
scale_fill_manual(values=c('grey', 'red')) +
theme_bw() +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
labs(x="Target Gene", y="Percent UMIs IPA", fill="Control")
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x="Perturbations", y=pct_ipa)) +
geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene),
pch=16) +
scale_color_manual(values=c("black", "red")) +
scale_size_manual(values=c(0.1,1), guide='none') +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
labs(x=NULL, y="Percent UMIs from IPA isoforms",
color="Control")## Warning: Ignoring unknown aesthetics: textggplotly(g, tooltip=c("text", "y")) %>%
style(hoverinfo = "skip", traces = 1)df_targets %>%
select(target_gene, pct_ipa, mean_umis_total, sgID_AB) %>%
slice_max(pct_ipa, n=20)## # A tibble: 20 × 4
## target_gene pct_ipa mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 AARS 0.197 8131. AARS_+_70323362.23-P1P2|AARS_-_70323332.…
## 2 SNRPD2 0.196 8254. SNRPD2_+_46195119.23-P1P2|SNRPD2_+_46195…
## 3 HSPA9 0.196 7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_137911…
## 4 CHMP3 0.195 9972. CHMP3_-_86790522.23-P1P2|CHMP3_-_8679045…
## 5 SARS 0.195 7045. SARS_+_109756585.23-P1P2|SARS_-_10975660…
## 6 TIMM23B 0.193 7987. TIMM23B_-_51371488.23-P1P2|TIMM23B_-_513…
## 7 PHB 0.193 7139. PHB_+_47492183.23-P1P2|PHB_+_47492231.23…
## 8 QARS 0.193 7764. QARS_-_49142149.23-P1P2|QARS_-_49142355.…
## 9 RTF1 0.193 7844. RTF1_-_41709376.23-P1P2|RTF1_-_41709401.…
## 10 PHB2 0.193 7284. PHB2_+_7079788.23-P1P2|PHB2_-_7079800.23…
## 11 EIF3D 0.192 8899. EIF3D_+_36925166.23-P1P2|EIF3D_-_3692515…
## 12 EIF2S1 0.192 7588. EIF2S1_-_67827085.23-P1P2|EIF2S1_-_67827…
## 13 LSM6 0.192 10412. LSM6_-_147096941.23-P1P2|LSM6_-_14709691…
## 14 SUPT16H 0.192 7730. SUPT16H_-_21851767.23-P1P2|SUPT16H_-_218…
## 15 SNRPD1 0.192 8701. SNRPD1_+_19192365.23-P1P2|SNRPD1_+_19192…
## 16 MED30 0.192 4428. MED30_+_118533216.23-P1P2|MED30_-_118533…
## 17 RBM22 0.192 10107. RBM22_-_150080586.23-P1P2|RBM22_-_150080…
## 18 EIF2B4 0.192 8104. EIF2B4_+_27593168.23-P1P2|EIF2B4_+_27593…
## 19 HARS 0.192 7389. HARS_+_140070923.23-P1P2|HARS_+_14007091…
## 20 PAF1 0.192 6175. PAF1_+_39881751.23-P1P2|PAF1_-_39881702.…df_targets %>%
select(target_gene, pct_ipa, mean_umis_total, sgID_AB) %>%
slice_min(pct_ipa, n=20)## # A tibble: 20 × 4
## target_gene pct_ipa mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 TAF6 0.174 7708. TAF6_-_99716931.23-P1P2|TAF6_+_99716935.…
## 2 CSTF3 0.174 8171. CSTF3_+_33183038.23-P1P2|CSTF3_-_3318280…
## 3 KRI1 0.175 9428. KRI1_+_10676632.23-P1P2|KRI1_-_10676612.…
## 4 RPS3 0.175 9594. RPS3_-_75110633.23-P1|RPS3_-_75110586.23…
## 5 NOP14 0.175 8507. NOP14_+_2965021.23-P1P2|NOP14_+_2965104.…
## 6 RPS4X 0.175 9604. RPS4X_+_71497070.23-P1P2|RPS4X_-_7149704…
## 7 UTP3 0.175 8015. UTP3_+_71554274.23-P1P2|UTP3_-_71554284.…
## 8 POP1 0.175 9301. POP1_-_99129573.23-P1P2|POP1_-_99129576.…
## 9 RPS19BP1 0.175 8611. RPS19BP1_-_39928789.23-P1P2|RPS19BP1_-_3…
## 10 RIOK2 0.175 9175. RIOK2_+_96518912.23-P1P2|RIOK2_+_9651890…
## 11 PPRC1 0.176 8350. PPRC1_-_103892809.23-P1P2|PPRC1_-_103892…
## 12 RRP12 0.176 8715. RRP12_+_99161057.23-P1P2|RRP12_-_9916103…
## 13 NOP58 0.176 8749. NOP58_-_203130504.23-P1P2|NOP58_-_203130…
## 14 DIMT1 0.176 8641. DIMT1_+_61699702.23-P1P2|DIMT1_+_6169966…
## 15 NEPRO 0.176 9597. C3orf17_-_112738463.23-P1P2|C3orf17_+_11…
## 16 CPSF4 0.176 6830. CPSF4_-_99036611.23-P1P2|CPSF4_-_9903657…
## 17 RPS24 0.176 9498. RPS24_+_79793627.23-P1P2|RPS24_+_7979369…
## 18 PDCD11 0.176 9118. PDCD11_-_105156445.23-P1P2|PDCD11_+_1051…
## 19 RPP40 0.176 8805. RPP40_-_5004221.23-P1P2|RPP40_+_5004190.…
## 20 UTP6 0.176 9415. UTP6_-_30228710.23-P1P2|UTP6_-_30228722.…Proximal
df_targets %>%
mutate(sgID_AB=fct_reorder(sgID_AB, -pct_proximal)) %>%
ggplot(aes(x=sgID_AB, y=pct_proximal, fill=target_gene == "non-targeting")) +
geom_bar(stat='identity') +
scale_y_continuous(expand=c(0,0,0.05,0)) +
scale_fill_manual(values=c('grey', 'red')) +
theme_bw() +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
labs(x="Target Gene", y="Percent UMIs Proximal Transcript", fill="Control")
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x="Perturbations", y=pct_proximal)) +
geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene),
pch=16) +
scale_color_manual(values=c("black", "red")) +
scale_size_manual(values=c(0.1,1), guide='none') +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
labs(x=NULL, y="Percent UMIs from proximal isoforms",
color="Control")## Warning: Ignoring unknown aesthetics: textggplotly(g, tooltip=c("text", "y")) %>%
style(hoverinfo = "skip", traces = 1)df_targets %>%
select(target_gene, pct_proximal, mean_umis_total, sgID_AB) %>%
slice_max(pct_proximal, n=20)## # A tibble: 20 × 4
## target_gene pct_proximal mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 NUDT21 0.102 9659. NUDT21_+_56485197.23-P1P2|NUDT21_-_…
## 2 CPSF6 0.100 8542. CPSF6_+_69633592.23-P1P2|CPSF6_+_69…
## 3 OGFOD1 0.0996 10061. OGFOD1_-_56485723.23-P1P2|OGFOD1_+_…
## 4 RPS8 0.0945 7730. RPS8_+_45241261.23-P1P2|RPS8_-_4524…
## 5 RPS4X 0.0938 9604. RPS4X_+_71497070.23-P1P2|RPS4X_-_71…
## 6 RPS13 0.0936 8821. RPS13_+_17099232.23-P1P2|RPS13_+_17…
## 7 RPS6 0.0936 8773. RPS6_+_19380221.23-P1P2|RPS6_+_1938…
## 8 PDCD11 0.0936 9118. PDCD11_-_105156445.23-P1P2|PDCD11_+…
## 9 RPS16 0.0936 9434. RPS16_+_39926539.23-P1P2|RPS16_-_39…
## 10 UTP6 0.0934 9415. UTP6_-_30228710.23-P1P2|UTP6_-_3022…
## 11 RPS3 0.0934 9594. RPS3_-_75110633.23-P1|RPS3_-_751105…
## 12 PPRC1 0.0933 8350. PPRC1_-_103892809.23-P1P2|PPRC1_-_1…
## 13 DDX47 0.0932 9380. DDX47_+_12966332.23-P1P2|DDX47_+_12…
## 14 RPS19 0.0932 8936. RPS19_-_42364369.23-P1P2|RPS19_+_42…
## 15 BMS1 0.0931 9359. BMS1_+_43278253.23-P1P2|BMS1_-_4327…
## 16 NOL6 0.0930 9283. NOL6_-_33473848.23-P1P2|NOL6_+_3347…
## 17 CMTR1 0.0930 8350. CMTR1_-_37401025.23-P1P2|CMTR1_-_37…
## 18 RPS24 0.0930 9498. RPS24_+_79793627.23-P1P2|RPS24_+_79…
## 19 RPS27A 0.0928 10186. RPS27A_-_55459862.23-P1P2|RPS27A_+_…
## 20 RPS18 0.0928 8946. RPS18_+_33239917.23-P1P2|RPS18_+_33…df_targets %>%
select(target_gene, pct_proximal, mean_umis_total, sgID_AB) %>%
slice_min(pct_proximal, n=20)## # A tibble: 20 × 4
## target_gene pct_proximal mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 RTF1 0.0746 7844. RTF1_-_41709376.23-P1P2|RTF1_-_4170…
## 2 RBM25 0.0779 8632. RBM25_+_73525283.23-P1P2|RBM25_-_73…
## 3 RBM22 0.0785 10107. RBM22_-_150080586.23-P1P2|RBM22_-_1…
## 4 CTR9 0.0786 6545. CTR9_+_10772865.23-P1P2|CTR9_+_1077…
## 5 PAF1 0.0790 6175. PAF1_+_39881751.23-P1P2|PAF1_-_3988…
## 6 NPAT 0.0795 11484. NPAT_-_108093360.23-P1P2|NPAT_+_108…
## 7 POT1 0.0798 8985. POT1_+_124569865.23-P1P2|POT1_-_124…
## 8 SMN2 0.0806 8704. SMN2_-_69345531.23-ENST00000380743.…
## 9 SFPQ 0.0808 7237. SFPQ_-_35658705.23-P1P2|SFPQ_-_3565…
## 10 SUPT16H 0.0808 7730. SUPT16H_-_21851767.23-P1P2|SUPT16H_…
## 11 SARS 0.0813 7045. SARS_+_109756585.23-P1P2|SARS_-_109…
## 12 SNRPD2 0.0814 8254. SNRPD2_+_46195119.23-P1P2|SNRPD2_+_…
## 13 EIF3D 0.0815 8899. EIF3D_+_36925166.23-P1P2|EIF3D_-_36…
## 14 PRPF40A 0.0818 8242. PRPF40A_+_153573726.23-P1P2|PRPF40A…
## 15 MTOR 0.0819 8818. MTOR_+_11322547.23-P1P2|MTOR_+_1132…
## 16 SRSF7 0.0820 9748. SRSF7_+_38978481.23-P1P2|SRSF7_-_38…
## 17 SRRT 0.0821 6632. SRRT_+_100472814.23-P1P2|SRRT_+_100…
## 18 HSPA9 0.0822 7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_1…
## 19 SLBP 0.0823 10994. SLBP_-_1713995.23-P1P2|SLBP_+_17136…
## 20 CASP8AP2 0.0823 10865. CASP8AP2_-_90539614.23-P1P2|CASP8AP…Distal
df_targets %>%
mutate(sgID_AB=fct_reorder(sgID_AB, -pct_distal)) %>%
ggplot(aes(x=sgID_AB, y=pct_distal, fill=target_gene == "non-targeting")) +
geom_bar(stat='identity') +
scale_y_continuous(expand=c(0,0,0.05,0)) +
scale_fill_manual(values=c('grey', 'red')) +
theme_bw() +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank()) +
labs(x="Target Gene", y="Percent UMIs Distal Transcripts", fill="Control")
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x="Perturbations", y=pct_distal)) +
geom_violin(fill='lightgrey', size=0.2, draw_quantiles=c(0.25, 0.50, 0.75)) +
geom_quasirandom(aes(color=is_nt, size=is_nt, text=target_gene),
pch=16) +
scale_color_manual(values=c("black", "red")) +
scale_size_manual(values=c(0.1,1), guide='none') +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
labs(x=NULL, y="Percent UMIs from distal isoforms",
color="Control")## Warning: Ignoring unknown aesthetics: textggplotly(g, tooltip=c("text", "y")) %>%
style(hoverinfo = "skip", traces = 1)df_targets %>%
select(target_gene, pct_distal, mean_umis_total, sgID_AB) %>%
slice_max(pct_distal, n=20)## # A tibble: 20 × 4
## target_gene pct_distal mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 TAF10 0.435 6087. TAF10_-_6633436.23-P1P2|TAF10_+_66334…
## 2 FIP1L1 0.435 6997. FIP1L1_+_54243867.23-P1P2|FIP1L1_-_54…
## 3 TAF2 0.432 6366. TAF2_-_120844853.23-P1P2|TAF2_-_12084…
## 4 TAF8 0.431 6889. TAF8_-_42018330.23-P1P2|TAF8_+_420183…
## 5 TAF12 0.431 6087. TAF12_+_28969568.23-P1P2|TAF12_+_2896…
## 6 PCF11 0.430 8077. PCF11_-_82868415.23-P1P2|PCF11_-_8286…
## 7 TAF1 0.430 6047. TAF1_-_70586444.23-P1P2|TAF1_+_705866…
## 8 TAF7 0.430 7110. TAF7_+_140700244.23-P1P2|TAF7_+_14070…
## 9 SRSF7 0.430 9748. SRSF7_+_38978481.23-P1P2|SRSF7_-_3897…
## 10 CPSF4 0.430 6830. CPSF4_-_99036611.23-P1P2|CPSF4_-_9903…
## 11 TAF6 0.430 7708. TAF6_-_99716931.23-P1P2|TAF6_+_997169…
## 12 NPAT 0.430 11484. NPAT_-_108093360.23-P1P2|NPAT_+_10809…
## 13 SRRT 0.429 6632. SRRT_+_100472814.23-P1P2|SRRT_+_10047…
## 14 UPF1 0.429 10350. UPF1_+_18942771.23-P1P2|UPF1_-_189428…
## 15 CSTF3 0.429 8171. CSTF3_+_33183038.23-P1P2|CSTF3_-_3318…
## 16 CPSF2 0.428 8938. CPSF2_-_92588375.23-P1P2|CPSF2_+_9258…
## 17 MRPS14 0.428 9269. MRPS14_-_174992444.23-P1P2|MRPS14_+_1…
## 18 TAF5 0.428 6165. TAF5_-_105127776.23-P1P2|TAF5_+_10512…
## 19 CASP8AP2 0.428 10865. CASP8AP2_-_90539614.23-P1P2|CASP8AP2_…
## 20 CPSF1 0.428 7674. CPSF1_+_145634685.23-P1P2|CPSF1_-_145…df_targets %>%
select(target_gene, pct_distal, mean_umis_total, sgID_AB) %>%
slice_min(pct_distal, n=20)## # A tibble: 20 × 4
## target_gene pct_distal mean_umis_total sgID_AB
## <chr> <dbl> <dbl> <chr>
## 1 NUDT21 0.385 9659. NUDT21_+_56485197.23-P1P2|NUDT21_-_56…
## 2 CPSF6 0.394 8542. CPSF6_+_69633592.23-P1P2|CPSF6_+_6963…
## 3 OGFOD1 0.395 10061. OGFOD1_-_56485723.23-P1P2|OGFOD1_+_56…
## 4 GATA1 0.408 3819. GATA1_-_48645022.23-P1P2|GATA1_+_4864…
## 5 NKAP 0.409 9616. NKAP_-_119077682.23-P1P2|NKAP_+_11907…
## 6 PHB 0.410 7139. PHB_+_47492183.23-P1P2|PHB_+_47492231…
## 7 PHB2 0.410 7284. PHB2_+_7079788.23-P1P2|PHB2_-_7079800…
## 8 RFFL 0.410 10038. RFFL_-_33416281.23-P1P2|RFFL_+_334162…
## 9 EIF2S3 0.410 7363. EIF2S3_-_24073117.23-P1P2|EIF2S3_+_24…
## 10 DNAJC17 0.411 9633. DNAJC17_-_41099633.23-P1P2|DNAJC17_+_…
## 11 HSPA9 0.411 7158. HSPA9_-_137911079.23-P1P2|HSPA9_-_137…
## 12 EIF2S1 0.411 7588. EIF2S1_-_67827085.23-P1P2|EIF2S1_-_67…
## 13 DDX6 0.411 8935. DDX6_+_118661671.23-P1P2|DDX6_+_11866…
## 14 CCT2 0.411 9295. CCT2_-_69979656.23-P1P2|CCT2_-_699792…
## 15 CNOT1 0.412 11112. CNOT1_+_58663696.23-P1P2|CNOT1_-_5866…
## 16 POLR3A 0.412 8672. POLR3A_-_79789274.23-P1P2|POLR3A_+_79…
## 17 AARS 0.412 8131. AARS_+_70323362.23-P1P2|AARS_-_703233…
## 18 TIMM44 0.412 9132. TIMM44_-_8008522.23-P1P2|TIMM44_+_800…
## 19 CCT7 0.412 9386. CCT7_-_73461472.23-P1P2|CCT7_-_734614…
## 20 CCT3 0.412 10074. CCT3_+_156308067.23-P1P2|CCT3_-_15630…Pair Plots
Proximal vs Distal
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x=pct_proximal, y=pct_distal, color=is_nt, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=c("black", "red")) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from proximal isoforms",
y="Percent UMIs from distal isoforms")
ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))Similar to the first pass, we observed three extreme outliers that have isoform switching effects: CPSF6, NUDT21, and OGFOD1. In the same direction - shortening when knocked down - we see CNOT1, PABPC1, and GATA1, but with milder effect sizes. In the opposite direction - lengthening when knocked-down - we see PCF11, SRSF7, FIP1L1, and NPAT.
Other factors appear to impact the isoforms almost independently. Namely, knockdown of TAF proteins have a strong increase in distal isoforms. Similarly, core factors, such as, CPSF1-4, CSTF1, and CSTF3.
The PAF1 complex members form an outlier set that reduces proximial isoform usage with little impact on distal usage, such as, RTF1, PAF1, and CTF9.
IPA vs Proximal
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x=pct_ipa, y=pct_proximal, color=is_nt, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=c("black", "red")) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from proximal isoforms")
ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))Again, the isoform switchers from above are extreme outliers. Here we see that knockdown of PAF1 complex members reduces proximal usage in favor of increased IPA usage. This is in contrast to GATA1 knockdown, which increase both IPA and proximal usage.
IPA vs Distal
g <- df_targets %>%
mutate(is_nt=target_gene == "non-targeting") %>%
ggplot(aes(x=pct_ipa, y=pct_distal, color=is_nt, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=c("black", "red")) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from distal isoforms")
ggplotly(g, tooltip=c("text", "x", "y", "n_cells"))Here we observe the factors whose knockdown increased distal usage independent of proximal usage do so at the cost of decrease IPA usage. These were the TAF members, CPSF1-4, and CSTF3. Notably, CPSF2 and CPSF3 do not appear to impact IPA.
Conclusion
Considering on multi-UTR genes provides a cleaner signal for effects on different isoforms. We now see many of the known factors stand out, and can identify other possible genes with isoform-specific effects.
Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
## [3] Biobase_2.54.0 GenomicRanges_1.46.0
## [5] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [7] S4Vectors_0.32.0 BiocGenerics_0.40.0
## [9] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [11] Matrix_1.3-4 ggbeeswarm_0.6.0
## [13] plotly_4.10.0 cowplot_1.1.1
## [15] forcats_0.5.1 stringr_1.4.0
## [17] dplyr_1.0.8 purrr_0.3.4
## [19] readr_2.1.1 tidyr_1.1.4
## [21] tibble_3.1.7 ggplot2_3.3.5
## [23] tidyverse_1.3.1 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
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## [4] httr_1.4.2 tools_4.1.1 backports_1.4.0
## [7] bslib_0.3.1 utf8_1.2.2 R6_2.5.1
## [10] vipor_0.4.5 DBI_1.1.1 lazyeval_0.2.2
## [13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
## [16] compiler_4.1.1 cli_3.3.0 rvest_1.0.2
## [19] xml2_1.3.3 DelayedArray_0.20.0 labeling_0.4.2
## [22] sass_0.4.0 scales_1.1.1 digest_0.6.29
## [25] rmarkdown_2.11 XVector_0.34.0 pkgconfig_2.0.3
## [28] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
## [31] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_1.0.2
## [34] readxl_1.3.1 rstudioapi_0.13 farver_2.1.0
## [37] jquerylib_0.1.4 generics_0.1.1 jsonlite_1.7.2
## [40] crosstalk_1.2.0 RCurl_1.98-1.5 GenomeInfoDbData_1.2.7
## [43] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [46] lifecycle_1.0.1 stringi_1.7.6 yaml_2.2.1
## [49] zlibbioc_1.40.0 grid_4.1.1 crayon_1.4.2
## [52] lattice_0.20-45 haven_2.4.3 hms_1.1.1
## [55] knitr_1.39 pillar_1.7.0 reprex_2.0.1
## [58] glue_1.6.2 evaluate_0.15 data.table_1.14.2
## [61] modelr_0.1.8 vctrs_0.4.1 tzdb_0.2.0
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## [67] xfun_0.30 broom_0.8.0 viridisLite_0.4.0
## [70] beeswarm_0.4.0 ellipsis_0.3.2Conda Environment
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